POSID: Target-Task-Unsupervised Person-Attribute Extraction from Heterogeneous Public-Safety Text
POSID is an unsupervised framework designed to extract structured attributes like gender or clothing from messy public-safety text without needing task-specific training. It works by breaking extraction into localization, scope control, and structured recovery steps. The results show it can reliably find person details even in complex, varied narratives.